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In nature, plants are exposed to a dynamic light environment. Fluctuations in light decreased the photosynthetic light utilization efficiency (PLUE) of leaves, and much more severely in C4 species than in C3 species. However, little is known about the plasticity of PLUE under dynamic light in C4 species. Present study focused on the influence of planting density to the photosynthesis under dynamic light in maize (Zea mays L.), a most important C4 crop. In addition, the molecular mechanism behind photosynthetic adaptation to planting density were also explored by quantitative proteomics analysis. Results revealed that as planting density increases, maize leaves receive less light that fluctuates more. The maize planted at high density (HD) improved the PLUE under dynamic light, especially in the middle and later growth stages. Quantitative proteomics analysis showed that the transfer of nitrogen from Rubisco to RuBP regeneration and C4 pathway related enzymes contributes to the photosynthetic adaptation to lower and more fluctuating light environment in HD maize. This study provides potential ways to further improve the light energy utilization efficiency of maize in HD. Summary statement: 1.As planting density increases, maize leaves receive less light that fluctuates more.2.The maize grown at high density improved the light utilization efficiency under dynamic light, especially in the middle and later growth stages.3.The transfer of nitrogen from Rubisco to RuBP regeneration and C4 pathway related enzymes contributes to the photosynthetic adaptation to lower and more fluctuating light environment in maize grown at high density. [ABSTRACT FROM AUTHOR]

GIS is prone to various defects and faults after putting into operation, and the failure rate is high. At present, the inspection methods of GIS in operation include traditional inspection mode, two-dimensional code inspection mode, radio frequency identification technology mode and robot inspection mode. However, the current inspection robot has the disadvantages of weak adaptability to complex environment, insufficient inspection scope, low efficiency and low intelligence. Therefore, this paper designs a GIS monitoring system based on the rail robot. By installing all kinds of sensors on the rail robot, with the help of binocular vision and mechanical arm, the omni-directional live detection of GIS in transit can be realized.

Compared with the traditional reactor abnormal state detection method, the acoustic vibration detection method combines the advantages of acoustic detection and vibration detection, which has the advantages of simple installation, strong portability and good signal integrity, and has a broad application prospect. In this paper, the characteristic parameters of reactor acoustic vibration, including sound pressure level, sound power level, acoustic signal spectrum, vibration amplitude, vibration acceleration value, vibration signal spectrum, harmonic proportion and frequency complexity, are obtained to analyze the range and distribution rule of each characteristic parameter under long-term operation condition of reactor, and the weight factor of each characteristic parameter is determined by analytic hierarchy process On the basis of fuzzy reasoning, the state evaluation model of reactor is established.

Although the state evaluation method based on characteristic parameters and weight factors can extract the characteristic quantities in time domain and frequency domain according to the collected acoustic and vibration signals of reactors, it is necessary to analyze a large number of test data to establish the functional relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states The method can directly learn the data samples, and self-study the correlation rules of characteristic parameters and defects through the training of neural network. In this paper, the deep learning neural network model is constructed, and the data obtained from reactor defect simulation experiment and field measurement are used as samples to train the deep learning network. Through the training of neural network, the characteristics of acoustic vibration signal are automatically learned, and the characteristics are stored in the parameters of neural network. Finally, the state of reactor is realized by the classifier at the end of the network assessment

Hydrogen sulfide, ammonia, formaldehyde, toluene, n-pentane and acetone were used as indoor gas pollutants. Using ozone synergistic photocatalysis technology, Ti02 was selected as photocatalyst and doped with Fe3+, and the effects of TiO2 modification and different gas flow rate, initial reactant concentration, relative humidity and ozone concentration on purification efficiency were investigated. The results show that when the gas flow rate in the reaction section is 1 m/s,the amount of ozone is 49.08 mg/m³, the relative humidity is less than 50%, and the system runs for 30 min, the average purification efficiency of 03/Fe-Ti02/UV technology for six kinds of gas pollutants is 65%. [ABSTRACT FROM AUTHOR]